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279
METHODOLOGIC ISSUES
Development of a multidisciplinary method to determine
risk factors for arm fracture in falls from playground
equipment
S Sherker, J Ozanne-Smith, G Rechnitzer, R Grzebieta
.............................................................................................................................
Injury Prevention 2003;9:279–283
See end of article for
authors’ affiliations
.......................
Correspondence to:
Shauna Sherker, Monash
University Accident
Research Centre, PO Box
70A, Monash University,
VIC 3800, Australia;
shauna.sherker@
general.monash.edu.au
.......................
P
Objectives: To present the development of a novel multidisciplinary method to investigate physical risk
factors for playground related arm fracture.
Rationale: Previous playground injury research has been limited in its ability to determine risk factors
for arm fractures, despite their common and costly occurrence. Biomechanical studies have focused
exclusively on head injury. Few epidemiological studies have quantified surface impact attenuation and
none have investigated specific injury outcomes such as arm fracture.
Design: An unmatched case-control study design was developed. An instrumented child dummy and
rig were designed to simulate real playground falls in situ. Validated output from the dummy was used
to quantify arm load. Other field measurements included equipment height, fall height, surface depth,
headform deceleration, and head injury criterion.
Discussion: Validated methods of biomechanics and epidemiology were combined in a robust design.
The principle strength of this method was the use of a multidisciplinary approach to identify and quantify risk and protective factors for arm fracture in falls from playground equipment. Application of this
method will enable countermeasures for prevention of playground related arm fracture to be
developed.
layground related injury is a serious and common event in
childhood, accounting for substantial morbidity and cost.
In Victoria Australia, over 5000 children present to hospital emergency departments each year and 22% are hospitalised with playground related injuries.1 This represents
approximately 6% of all hospital treated child injuries1 and
medical treatment costs of AUD$7.9 million per year in Victoria (1996/97).2
Playground injury most often results from falls from equipment, with arm fracture accounting for 43% of emergency
department presentations and 74% of hospital admissions.1
Most injury occurs to children aged less than 13 years, chiefly
in school playgrounds.1
Risk factors include equipment height and the type of surfacing onto which children fall.3–7 There is no consensus on the
most effective maximum height of playground equipment to
minimise risk of injury. Recommendations range from 1.5 m4
to 4.0 m.8 There is also debate on the most effective
surface.9 10
Limitations in previous analytical studies include: not
quantifying the impact attenuation of surfacing3–5 7; measurement bias in using height of equipment as a proxy for fall
height4–7; and limited statistical power for specific injury
outcomes, especially arm fracture.3–6
The head injury criterion (HIC) is a measure of the
probability of skull fracture in adults and correlates with the
magnitude of deceleration and duration of impact.11 As the
HIC was developed from adult cadaver tests, generalisation to
the probability of injury in children has been questioned.12
Laboratory based measures of HIC are nonetheless used to
define surface and height requirements for playground safety,
where peak deceleration below 200 g and HIC values below
1000 are recommended.8 13 14 Current playground impact tests
have limited generalisability due to their specificity for head
injury risk.
To date, no studies have addressed the specific risk factors
for arm fracture. Public health research in playground injury
has almost exclusively used epidemiological methods. Few
studies have integrated biomechanical and epidemiological
methods in injury prevention research, a combination recommended to bridge the gap between problem identification and
technological solution.15
Our aim was to combine the best methods of epidemiology
and biomechanics to investigate and quantify fall height and
surface impact characteristics as risk factors for arm fracture
in children falling from playground equipment.
METHODS AND RESULTS
Design
An unmatched case-control study (fig 1) was the most appropriate design in terms of specificity, effect size, potential bias,
cost, and timeframe.16 Upon review of epidemiological data,1
the study base was children aged less than 13 years who fell
from playground equipment located in primary schools and
preschools and landed on their arm.
Cases were defined as children under 13 who sustained an
upper limb fracture as a result of falling from playground
equipment at a school or preschool. Fractured long bones of
the upper extremity (humerus, radius, and ulna) represented
the largest and most costly group of playground related
injury.1 2 Cases included diagnoses corresponding to ICD10-AM codes S42.2, S42.3, S42.4 (fracture of humerus), S52
(fracture of forearm, including radius and/or ulna), and T10
(fracture of upper limb, level unspecified).17
Controls were children of the same age, in the same
settings, who also fell from playground equipment and landed
on their arm but did not suffer an arm fracture.
Sample size estimates were based on the following assumptions: alpha error = 0.05; beta error = 0.20 (80% power); and
.............................................................
Abbreviations: CoG, centre of gravity; HIC, head injury criterion
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280
Sherker, Ozanne-Smith, Rechnitzer, et al
Table 1
Variables measured for each fall event
Child variables
Playground
variables
Age
Gender
Equipment type
Equipment height
Height
Weight
Body mass index
Handedness
Surface
Surface
Surface
Surface
type
depth
moisture
age
Fall variables
Position at start of fall
Fall height (based on child
CoG)
Peak deceleration (g)
Head injury criteria
Arm load (kN)
Children were asked: From which piece of equipment did you
fall?; What were you doing just before you fell?; and Where did you
land?. Height and clothed weight of the child were measured
and handedness recorded.
Interview questions were designed for their simplicity and
specificity. To ascertain the validity of child self report, child
interview responses were compared to those of an adult
witness and to details from school injury reports, where available. Preliminary results indicate good child recall and no differential recall bias between cases and controls.
Field test protocols
Figure 1
Case-control study design.
a “clinically important” odds ratio of 2.0.18 A random sample
of 176 pieces of playground equipment were audited to determine compliance with the currently recommended safety
guidelines and to estimate children’s exposure to the risk factors of interest (equipment height and surface characteristics). Prevalence of non-compliant playgrounds was 90%.
Sample size estimates specified that 533 cases and 533
controls were required.19
Five hospitals were selected for case recruitment, representing 38% of all playground related injury admissions in
Victoria.1 Ethics committee approval was obtained to access
patient medical records. Hospital emergency department staff
checked patient records weekly to identify children meeting
the eligibility criteria. Children with non-confirmed fractures
of the long bones of the upper extremity were excluded.
Control schools were recruited randomly from the catchment area of participating hospitals. Feasibility of control
ascertainment was estimated from the annual number of
non-fracture injuries from Department of Education data
sources. Based on a reported recruitment rate for controls of
89% in a similar study,4 34 schools were required. Staff at control schools were trained to record playground falls using a
standard confidential incident report form.
An ascertainment protocol was designed to recruit cases
and controls. Upon notification of cases and controls and
written parental consent, an interview with the child was
arranged as soon as possible after their fall. National ethical
guidelines were followed.20
Interview protocol
An interview protocol was developed to determine from which
equipment the child fell, the fall height, and the location
where their arm impacted the playground surface. Case and
control interviews used identical protocols.
Children were interviewed at the fall site in the playground
and reported their own fall details. This improves on previous
study designs where parents or other proxies completed a
questionnaire regarding the child’s fall, often using a
photograph to identify fall location.3 6
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Playground safety standards8 13 14 were reviewed to develop
field test protocols with the objective of measuring the physical characteristics of the playground equipment and surface
where the child fell.
Field measurements are shown in table 1. Depth of surface
material was calculated as the average of three probe readings
taken 30 cm apart in a triangle around the point of contact.14
To better describe surface characteristics, moisture content
(dry, residual moisture, wet), age (weeks since installation),
and surface substrate were also recorded.
Equipment height was defined as the vertical distance from
the surface to the highest accessible part of the structure.21
Child fall height was the vertical distance between the
playground surface and the child’s centre of gravity (CoG) at
the start of their fall. CoG tables were derived based on child
anthropometric data,22 23 adapted for the various positions
commonly reported at the start of children’s falls (for
example, standing, hanging from arms).
Inter-rater reliability of field test measurements between
two testers averaged 0.9904 in a random sample of 46
playgrounds.
Development of instrumentation
Headform
A 5.4 kg child headform instrumented with a triaxial accelerometer (Playground Clearing House MAX G/SI) was used to
determine critical deceleration and HIC as standard measures
of playground safety.14 Drop tests in the field were conducted
from three heights: 1 m, child fall height, and maximum
equipment height.
Novel impact arm load dummy
The headform impact tests were limited by their specificity to
the risk of head injury. Review of playground injury epidemiology indicated that impact arm loads should be quantified in
order to specifically investigate the risk factors for arm
fracture.
An exhaustive search of existing anthropometric models
did not identify an appropriate dummy to investigate the risk
of child arm fracture. Thus, an anthropometric dummy capable of measuring impact arm loads was built according to
height and weight data for the proposed sample (fig 2).
The size and shape of the dummy was based on a surrogate
6 year old.24 The dummy was constructed of 6 mm steel with
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Risk factors for arm fracture in falls from playground equipment
281
Figure 2 Instrumented
anthropometric child falls dummy (left)
and drop rig (right).
shoulder breadth 285 mm, arm length 420 mm, and the palm
75 × 65 mm. The dummy alone weighed 10 kg and purpose
built durable plate weights were added to simulate the mass of
a child up to 50 kg (95th percentile for children <13 years).
The dummy was instrumented with a full bridge foil strain
gauge forming a load cell to measure 0.5 kN to 25 kN axial
loads. The required range and sensitivity of the load cell were
determined by pilot testing on various surface materials in the
laboratory. The sensor was placed in the forearm of the
dummy, just above the hand plate, and calibrated at regular
intervals. The dummy arm without the sensor was shorter
than that with the sensor to ensure the capture and estimation
of the full axial load on the child’s arm in the fall. To simulate
the child’s fall, drop tests in the field were conducted from the
child’s actual fall height.
A manual trigger was depressed to initiate data sampling.
The analogue output from the load cell was sampled at 20 000
Hz using a 16 bit analogue-to-digital converter. The digital
signal was amplified (100:1) and interfaced with a laptop PC
running custom written data acquisition software (HP VEE
version 5.01).
A rugged laptop PC (GETAC A-7407) was selected for its
durability and all-weather field testing capability. A computer
data acquisition card (DT300) with a +5V dc 1 Amp supply
line was used to power the load cell. The data acquisition system was fully battery operated by one tester in the field.
Drop test rig
A portable drop test rig was designed to lift each drop device
to the child’s fall height and release it into free fall to the surface (fig 2). This provided reliable, repeatable measurements
Figure 3 Results of playground headform validation tests
conducted at 1 m, 1.25 m, and 1.5 m drop heights.
and met occupational health and safety regulations for the
operators.
The rig was constructed to lift up to 50 kg to a maximum
height of 3.5 m (upper range of equipment height from playground equipment audit). It could be disassembled into eight
relatively flat sections for ease of transport.
Each impact test device (headform and arm load dummy)
was attached to the rig via a quick release mechanism, selected
for its ability to open with minimal extraneous perturbation of
the test device sensors.
Validation of instrumentation
The validity of the headform output was tested under dynamic
impact conditions at the Rosebank bicycle helmet laboratory
in Hallam, Victoria. The playground headform was tested at
heights of 1.0 m, 1.25 m, and 1.5 m and compared to the output of the Rosebank headform (gold standard). The correlation between the deceleration output of the playground headform and the gold standard was 0.9732 (fig 3).
The dummy arm load cell was validated against standard
instrumentation under static and dynamic laboratory conditions. Repeatability analysis (test-retest) of the load cell was
performed.
Static validation tests were conducted using a Baldwin Universal Testing Machine, calibrated according to the National
Association of Testing Authorities.25 A known direct compressive load was applied to the dummy arm and output from the
load cell was measured three times for each incremental load.
Figure 4 Response of dummy arm load cell to known applied
loads under static conditions.
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282
Sherker, Ozanne-Smith, Rechnitzer, et al
Table 2
Static dummy strain gauge responses to known applied loads
Applied load
(kN)
Output 1
(kN)
Output 2
(kN)
Output 3
(kN)
Output
average (kN) Error (%)
Repeatability
(%)
0.00
5.00
10.00
15.00
20.00
0.09
5.04
10.05
15.23
20.26
0.12
5.05
10.00
14.81
20.01
0.09
4.79
9.92
14.93
20.01
0.10
4.96
9.99
14.99
20.09
–
5.20
1.30
2.80
1.25
Correlation between output from the dummy load cell and
the expected output for static loads up to 20 kN was 0.9997
(fig 4). The error between observed and expected output
ranged from 0.07–0.79%, averaging less than 1% for each
tested load. The repeatability of the three measures for each
load ranged from 1.25%–5.20% (table 2).
Dynamic validation tests were conducted using an instrumented, calibrated Impulse Hammer as a pendulum (model
086C50, PCB Piezotronics, Buffalo, NY). Laboratory tests were
conducted using a soft hammer interface to approximate the
conditions of the playground surface at equivalent fall heights
of 0.39 m, 0.5 m, and 1.0 m.
Dynamic loads were imparted to the dummy arm with
expected load inputs measured directly from the hammer.
Correlation was 0.9838 for expected load compared to arm
load output up to 3.5kN (fig 5). The load cell was calibrated at
regular intervals throughout the data collection period.
Analysis methods
Raw field test data were imported into Excel and arm
load/time and deceleration/time traces were generated. From
these traces, peak load, peak deceleration, and HIC were
calculated.14 The highest reading (worst outcome) of three
drop tests at each height was selected for further analysis.14
Once data collection is complete, analysis of the casecontrol study data will include univariate odds ratio calculations for each risk factor to identify potential effect modifiers
and confounding factors, and multivariate analysis (logistic
regression) to assess risk factors while controlling for
confounders. The main variables of interest are equipment
height, fall height, and surface impact characteristics.
DISCUSSION
The main strength of this method is the use of a multidisciplinary approach to identify the risk and protective factors for
arm fracture in falls from playground equipment. The
epidemiology/engineering interface enables risk factors to be
reliably specified and their physical characteristics to be quantified.
Figure 5 Response of dummy arm load cell under dynamic impact
conditions.
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–
−0.79
−0.10
−0.07
0.45
Given the predominance of arm fractures resulting from
falls from playground equipment, it was appropriate to
develop an alternative measure to HIC more closely related to
injury risk. Real world playground falls were recreated and
peak deceleration and arm load quantified to determine the
probability of arm fracture. The measures of fall height were
based on the child’s CoG and equipment height.
In selecting controls who fell and landed on their arm, all
subjects were exposed to the playground height and surface
risk factors of interest. There was a potential bias towards
more serious falls (but not fractures) among controls, because
children who were hurt were more likely to come to the
teachers’ attention. School staff were instructed to identify
any children falling from playground equipment, not only
children presenting with minor injury. Controls who presented to hospital were excluded. To assist with compliance,
upon completion of the schools’ commitment to the study, free
playground surface material was provided to control schools.
Controls were from a randomised sample of schools. Cases
who fell at control schools were included, allowing cases and
controls to potentially fall on the same playground. The same
playground did not necessarily equate to the same fall characteristics, which were considered unique for each fall event.
Equipment height, fall height, and surface depth were widely
represented in this study design. Since the laws of Newtonian
physics are universal, the results are generalisable.
Limitations
The playground falls dummy, although modeled on the
dimensions of the upper limb of a typical 6 year old child, did
not conform to the exact biofidelity of the human arm. The
dummy arm did not have a wrist or elbow joint and
represented a relatively “stiff” surrogate. The stiffness of the
dummy arm represented the worst case scenario of a fall onto
an outstretched arm with joints locked and maximal loading
on the long bones.
Application
These methods will identify risk and protective factors for arm
fracture in children who fall from playground equipment, in
particular the relationship between fall height and surface
impact characteristics and risk of arm fracture. The arm load
data compliments that obtained from the playground headform and adds to the limited body of knowledge about the
biomechanics of child arm fractures.
The arm load data will underpin the development of a
quantified arm fracture criterion. The future arm fracture criterion, when established as for HIC, will be applicable not only
to the testing of playground and other surfaces, but also to the
development of protective equipment, with potential application to sports and recreational injury prevention.
Future potential studies
This method could be further developed using computer modeling such as MADYMO to enable investigation of various fall
heights and surface characteristics and to determine optimum
playground conditions for injury prevention. A detailed review
of case medical records could illuminate the relationship
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Risk factors for arm fracture in falls from playground equipment
Key points
• Previous playground injury research has been limited in its
ability to determine risk factors for arm fracture, despite
their common and costly occurrence.
• A robust multidisciplinary method is presented to investigate
and quantify physical risk factors for arm fracture in
children who fall from playground equipment.
• The application of this method will determine critical equipment height and surface impact characteristics to inform
arm fracture prevention and playground safety standards.
between fall impact biomechanics and the clinical severity of
arm fracture to further inform playground standards. A study
to determine the effective impact attenuation of organic
surface materials over time is needed to guide maintenance
schedules.
ACKNOWLEDGEMENTS
This work is funded by the National Health and Medical Research
Council (project number 124414), the Victorian Health Promotion
Foundation and the Department of Human Services. We thank Chris
Powell and Roger Doulis, Monash University Department of Civil
Engineering, for technical advice in developing the instrumentation;
Jim Nixon, University of Queensland Department of Paediatrics and
Child Health, for loan of the playground headform; Len Koss, Monash
University Department of Mechanical Engineering, for use of the
Impact Hammer; Wendy Watson, Monash University Accident
Research Centre, for coordinating the playground equipment audit;
and Lesley Day, Monash University Accident Research Centre, for providing valuable comments on the methods.
.....................
Authors’ affiliations
S Sherker, J Ozanne-Smith , G Rechnitzer, Accident Research Centre,
Monash University, Melbourne, Australia
R Grzebieta , Department of Civil Engineering, Monash University,
Melbourne, Australia
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Development of a multidisciplinary method to
determine risk factors for arm fracture in falls
from playground equipment
S Sherker, J Ozanne-Smith, G Rechnitzer and R Grzebieta
Inj Prev 2003 9: 279-283
doi: 10.1136/ip.9.3.279
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